In addition, many previous RL models often suffer from generating unreal and inappropriate structures.
Docking score maximization alone is not sufficient to qualify molecules as drug candidates, as the molecules should satisfy strong structural constraints such as chemical realisticness and pharmacochemical suitability.
In other words, small molecule drug candidates should have enough steric stability to arrive at the target organ in the intended form (chemical realisticness), and they should not include any seriously reactive or toxic substructure (pharmacochemical suitability).
The low quality of generated molecules can arise from a single improper addition of atoms and bonds, which would deteriorate the entire sanity of the structure.
Since such `mistakes' are easy to occur, implicitly guiding the model (e.g., jointly training the model with QED optimization) cannot completely prevent the mistakes.
Thus, explicitly restricting the generation space within realistic and qualified molecules by generating molecules as a combination of appropriate fragments can be a promising solution [1]}, [2]}, [3]}.
